Molecular simulation studies of hydrogen enriched methane (HEM) storage in Covalent Organic Frameworks

Instead of separating hydrogen and methane mixture, such as synthetic gas, to obtain pure hydrogen and methane as clean fuel, hydrogen enriched methane (abbreviated as HEM) storage in 33 different Covalent Organic Frameworks (COFs) were studied for the first time near ambient temperatures using Grand Canonical Monte Carlo (GCMC) simulation. The use of HEM for on-board combustion engine is also known to be able to improve combustion performance as well as decrease noxious emissions. HEM adsorption performance in the COFs was mainly evaluated from three different aspects: volumetric energy density of combustion of stored HEM, gravimetric energy density of combustion of stored HEM and hydrogen selectivity. Several properties of the COFs, such as surface area, porosity, pore size were calculated for establishing the correlation with the HEM adsorption performance. The effect of temperature, initial hydrogen/methane bulk composition and hydrogen and methane/hydrogen’s heat of adsorption (HOA) in COFs on the performance of HEM adsorption were also investigated. Our work suggested there exists a complex interplay of the properties of the COFs, temperature and bulk composition which influence the energy density of the adsorbed HEM as well as methane and hydrogen ratio in the adsorbed phase

RepViT: Revisiting Mobile CNN From ViT Perspective

Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on resource-constrained mobile devices. This improvement is usually attributed to the multi-head self-attention module, which enables the model to learn global representations. However, the architectural disparities between lightweight ViTs and lightweight CNNs have not been adequately examined. In this study, we revisit the efficient design of lightweight CNNs and emphasize their potential for mobile devices. We incrementally enhance the mobile-friendliness of a standard lightweight CNN, specifically MobileNetV3, by integrating the efficient architectural choices of lightweight ViTs. This ends up with a new family of pure lightweight CNNs, namely RepViT. Extensive experiments show that RepViT outperforms existing state-of-the-art lightweight ViTs and exhibits favorable latency in various vision tasks. On ImageNet, RepViT achieves over 80\% top-1 accuracy with nearly 1ms latency on an iPhone 12, which is the first time for a lightweight model, to the best of our knowledge. Our largest model, RepViT-M3, obtains 81.4\% accuracy with only 1.3ms latency. The code and trained models are available at \url{https://github.com/jameslahm/RepViT}.Comment: 9 pages, 7 figure

Transcriptome and expression profiling analysis revealed changes of multiple signaling pathways involved in immunity in the large yellow croaker during Aeromonas hydrophila infection

<p>Abstract</p> <p>Background</p> <p>The large yellow croaker (<it>Pseudosciaena crocea</it>) is an economically important marine fish in China suffering from severe outbreaks of infectious disease caused by marine bacteria such as <it>Aeromonas hydrophila </it>(<it>A. hydrophila</it>), resulting in great economic losses. However, the mechanisms involved in the immune response of this fish to bacterial infection are not fully understood. To understand the molecular mechanisms underlying the immune response to such pathogenic bacteria, we used high-throughput deep sequencing technology to investigate the transcriptome and comparative expression profiles of the large yellow croaker infected with <it>A. hydrophila</it>.</p> <p>Results</p> <p>A total of 13,611,340 reads were obtained and assembled into 26,313 scaffolds in transcriptional responses of the <it>A. hydrophila</it>-infected large yellow croaker. Via annotation to the NCBI database, we obtained 8216 identified unigenes. In total, 5590 (68%) unigenes were classified into Gene Ontology, and 3094 unigenes were found in 20 KEGG categories. These genes included representatives from almost all functional categories. By using Solexa/Illumina's DeepSAGE, 1996 differentially expressed genes (P value < 0.05) were detected in comparative analysis of the expression profiles between <it>A. hydrophila</it>-infected fish and control fish, including 727 remarkably upregulated genes and 489 remarkably downregulated genes. Dramatic differences were observed in genes involved in the inflammatory response. Bacterial infection affected the gene expression of many components of signaling cascades, including the Toll-like receptor, JAK-STAT, and MAPK pathways. Genes encoding factors involved in T cell receptor (TCR) signaling were also revealed to be regulated by infection in these fish.</p> <p>Conclusion</p> <p>Based on our results, we conclude that the inflammatory response may play an important role in the early stages of infection. The signaling cascades such as the Toll-like receptor, JAK-STAT, and MAPK pathways are regulated by <it>A. hydrophila </it>infection. Interestingly, genes encoding factors involved in TCR signaling were revealed to be downregulated by infection, indicating that TCR signaling was suppressed at this early period. These results revealed changes of multiple signaling pathways involved in immunity during <it>A. hydrophila </it>infection, which will facilitate our comprehensive understanding of the mechanisms involved in the immune response to bacterial infection in the large yellow croaker.</p

Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, \ie, CLIP, to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://github.com/jameslahm/SCPNet.Comment: Accepted by IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 202

tert-Butyl N-(4-hy­droxy­benz­yl)-N-[4-(prop-2-yn­yloxy)benz­yl]carbamate

In the crystal structure of the title compound, C22H25NO4, inter­molecular O—H⋯O hydrogen bonds involving the hy­droxy group of the 4-(amimometh­yl)phenol fragment and the C=O group connect the mol­ecules into infinite chains along the c axis. Two C atoms of the propyne group are disordered over two sites with occupancy factors of 0.53 (2) and 0.47 (2)

Assessment of continuous fermentative hydrogen and methane co-production using macro- and micro-algae with increasing organic loading rate

A two-stage continuous fermentative hydrogen and methane co-production using macro-algae (Laminaria digitata) and micro-algae (Arthrospira platensis) at a C/N ratio of 20 was established. The hydraulic retention time (HRT) of first-stage H2 reactor was 4 days. The highest specific hydrogen yield of 55.3 mL/g volatile solids (VS) was obtained at an organic loading rate (OLR) of 6.0 gVS/L/d. In the second-stage CH4 reactor at a short HRT of 12 days, a specific methane yield of 245.0 mL/gVS was achieved at a corresponding OLR of 2.0 gVS/L/d. At these loading rates, the two-stage continuous system offered process stability and effected an energy yield of 9.4 kJ/gVS, equivalent to 77.7% of that in an idealised batch system. However, further increases in OLR led to reduced hydrogen and methane yields in both reactors. The process was compared to a one-stage anaerobic co-digestion of algal mixtures at an HRT of 16 days. A remarkably high salinity level of 13.3 g/kg was recorded and volatile fatty acid accumulations were encountered in the one-stage CH4 reactor. The two-stage system offered better performances in both energy return and process stability. The gross energy potential of the advanced gaseous biofuels from this algal mixture may reach 213 GJ/ha/yr